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<a href="_cross_entropy_method_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       Implements the Cross Entropy Algorithm.</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * </span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * Christophe Thiery, Bruno Scherrer. Improvements on Learning Tetris with Cross Entropy.</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * International Computer Games Association Journal, ICGA, 2009, 32. &lt;inria-00418930&gt;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      Jens Holm, Mathias Petræus and Mark Wulff</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        January 2016</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_DIRECT_SEARCH_CROSSENTROPY_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_ALGORITHMS_DIRECT_SEARCH_CROSSENTROPY_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_d_l_l_support_8h.html">shark/Core/DLLSupport.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_single_objective_optimizer_8h.html">shark/Algorithms/AbstractSingleObjectiveOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_random_8h.html">shark/Core/Random.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_individual_8h.html">shark/Algorithms/DirectSearch/Individual.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;boost/shared_ptr.hpp&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"></span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \brief Implements the Cross Entropy Method.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  This class implements the noisy cross entropy method </span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///  as descibed in the following article.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///  Christophe Thiery, Bruno Scherrer. Improvements on Learning Tetris with Cross Entropy.</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///  International Computer Games Association Journal, ICGA, 2009, 32. &lt;inria-00418930&gt;</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///  The algorithm aims to minimize an objective function through stochastic search.</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///  It works iteratively until a certain stopping criteria is met. At each </span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///  iteration, it samples a number of vectors from a Gaussian distribution</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///  and evaluates each of these against the supplied objective function.</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///  Based on the return value from the objective function, a subset of the </span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///  the best ranked vectors are chosen to update the search parameters </span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///  of the next generation. </span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">///</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">///  The mean of the Gaussian distribution is set to the centroid of the best ranked </span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">///  vectors, and the variance is set to the variance of the best ranked </span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">///  vectors in each individual dimension.</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// \ingroup singledirect</span></div>
<div class="foldopen" id="foldopen00068" data-start="{" data-end="};">
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html">   68</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy_method.html" title="Implements the Cross Entropy Method.">CrossEntropyMethod</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>{</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment"></span> </div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">    /// \brief Interface class for noise type.</span></div>
<div class="foldopen" id="foldopen00073" data-start="{" data-end="};">
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html">   73</a></span><span class="comment"></span>    <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html" title="Interface class for noise type.">INoiseType</a> {</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    <span class="keyword">public</span>:</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html#aeb003589c15cf765f869f41c02ea731b">   75</a></span>        <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html#aeb003589c15cf765f869f41c02ea731b">noiseValue</a> (<span class="keywordtype">int</span> t)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 0.0; };</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html#acedfc8d3e4b0665558d1514664b68e21">   76</a></span>        <span class="keyword">virtual</span> std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html#acedfc8d3e4b0665558d1514664b68e21">name</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> std::string(<span class="stringliteral">&quot;Default noise of 0&quot;</span>); }</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    };</div>
</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment"></span> </div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    /// \brief Smart pointer for noise type.</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a5cd00f92f182f8045837763e93fc04c6">   80</a></span><span class="comment"></span>    <span class="keyword">typedef</span> boost::shared_ptr&lt;INoiseType&gt; <a class="code hl_typedef" href="classshark_1_1_cross_entropy_method.html#a5cd00f92f182f8045837763e93fc04c6" title="Smart pointer for noise type.">StrongNoisePtr</a>;</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment"></span> </div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    /// \brief Constant noise term z_t = noise.</span></div>
<div class="foldopen" id="foldopen00083" data-start="{" data-end="};">
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html">   83</a></span><span class="comment"></span>    <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html" title="Constant noise term z_t = noise.">ConstantNoise</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html" title="Interface class for noise type.">INoiseType</a> {</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="keyword">public</span>:</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#a0af64a18dea0a2cbeb8a281ad4e6ba64">   85</a></span>        <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#a0af64a18dea0a2cbeb8a281ad4e6ba64">ConstantNoise</a> ( <span class="keywordtype">double</span> <a class="code hl_variable" href="_mc_svm_linear_8cpp.html#a607bc7e80f2934c0edc34bb19b401332">noise</a> ) { m_noise = <a class="code hl_variable" href="_mc_svm_linear_8cpp.html#a607bc7e80f2934c0edc34bb19b401332">noise</a>; };</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#ada2ba5e9b24cfab36ee9cd6e25447f6e">   86</a></span>        <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#ada2ba5e9b24cfab36ee9cd6e25447f6e">noiseValue</a> (<span class="keywordtype">int</span> t)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> std::max(m_noise, 0.0); }</div>
<div class="foldopen" id="foldopen00087" data-start="{" data-end="}">
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#aa82322d10dcf82474c7bf8fdce4fcfd0">   87</a></span>        <span class="keyword">virtual</span> std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_constant_noise.html#aa82322d10dcf82474c7bf8fdce4fcfd0">name</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            std::stringstream ss;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            ss &lt;&lt; <span class="stringliteral">&quot;z(t) = &quot;</span> &lt;&lt; m_noise;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            <span class="keywordflow">return</span> std::string(ss.str());</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        }</div>
</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    <span class="keyword">private</span>:</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <span class="keywordtype">double</span> m_noise;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    };</div>
</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment"></span> </div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    /// \brief Linear noise term z_t = a + t / b.</span></div>
<div class="foldopen" id="foldopen00097" data-start="{" data-end="};">
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html">   97</a></span><span class="comment"></span>    <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html" title="Linear noise term z_t = a + t / b.">LinearNoise</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html" title="Interface class for noise type.">INoiseType</a> {</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    <span class="keyword">public</span>:</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#a7c766e1314d0c921ccf2a6d2bbed76d5">   99</a></span>        <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#a7c766e1314d0c921ccf2a6d2bbed76d5">LinearNoise</a> ( <span class="keywordtype">double</span> a, <span class="keywordtype">double</span> b ) { m_a = a; m_b = b; };</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#a3ea315bb004fc14c6c23f909f4c49a1a">  100</a></span>        <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#a3ea315bb004fc14c6c23f909f4c49a1a">noiseValue</a> (<span class="keywordtype">int</span> t)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> std::max(m_a + (t * m_b), 0.0); }</div>
<div class="foldopen" id="foldopen00101" data-start="{" data-end="}">
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#ae6ef7e2b639f5d30b056e7af4995d4f7">  101</a></span>        <span class="keyword">virtual</span> std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_method_1_1_linear_noise.html#ae6ef7e2b639f5d30b056e7af4995d4f7">name</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>            std::stringstream ss;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>            std::string sign = (m_b &lt; 0.0 ? <span class="stringliteral">&quot; - &quot;</span> : <span class="stringliteral">&quot; + &quot;</span>);</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            ss &lt;&lt; <span class="stringliteral">&quot;z(t) = &quot;</span> &lt;&lt; m_a &lt;&lt; sign &lt;&lt; <span class="stringliteral">&quot;t * &quot;</span> &lt;&lt; std::abs(m_b);</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>            <span class="keywordflow">return</span> std::string(ss.str());</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        }</div>
</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    <span class="keyword">private</span>:</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordtype">double</span> m_a, m_b;</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    };</div>
</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    </div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"></span> </div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// \brief Default c&#39;tor.</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a6cbaa9d9b87109f7616bead343cf3a93">  113</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a6cbaa9d9b87109f7616bead343cf3a93" title="Default c&#39;tor.">CrossEntropyMethod</a>();</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment"></span> </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00116" data-start="{" data-end="}">
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#aa968320f9b938c9367eaab5cac3eeb6c">  116</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#aa968320f9b938c9367eaab5cac3eeb6c" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;Cross Entropy Method&quot;</span>; }</div>
</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment"></span> </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    /// \brief Sets default value for Population size.</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#adad84b965331a4c66edf77b64a03532d">  120</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#adad84b965331a4c66edf77b64a03532d" title="Sets default value for Population size.">suggestPopulationSize</a>(  ) ;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment"></span> </div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    /// \brief Calculates selection size for the supplied population size.</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a9d185dcc48d6f045bb5bdb278c2d90ee">  123</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9d185dcc48d6f045bb5bdb278c2d90ee" title="Calculates selection size for the supplied population size.">suggestSelectionSize</a>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#aaa8990eaf10820245651f85d811f8b6b" title="Returns a immutable reference to the size of the population.">populationSize</a> ) ;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span> </div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a51ac45a57be33ee94e76fc7024870628">  125</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a51ac45a57be33ee94e76fc7024870628" title="Read the component from the supplied archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive );</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a908b9eccea031c98b26a46357952abd7">  126</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a908b9eccea031c98b26a46357952abd7" title="Write the component to the supplied archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive ) <span class="keyword">const</span>;</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span> </div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a69e6fc00f991cbd03a5adf5e34b2d963" title="Initializes the algorithm for the supplied objective function and the initial mean p.">::init</a>;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"></span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// \brief Initializes the algorithm for the supplied objective function and the initial mean p.</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a69e6fc00f991cbd03a5adf5e34b2d963">  131</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a69e6fc00f991cbd03a5adf5e34b2d963" title="Initializes the algorithm for the supplied objective function and the initial mean p.">init</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; p);</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment"></span> </div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    /// \brief Initializes the algorithm for the supplied objective function.</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#af00780727abed9ac2563ce6cac472012">  134</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#af00780727abed9ac2563ce6cac472012" title="Initializes the algorithm for the supplied objective function.">init</a>(</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function,</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; initialSearchPoint,</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#aaa8990eaf10820245651f85d811f8b6b" title="Returns a immutable reference to the size of the population.">populationSize</a>,</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a6644bf8a37821ba5bf71c442b99d61ab" title="Returns the size of the parent population.">selectionSize</a>,</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        RealVector initialSigma</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    );</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment"></span> </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment">    /// \brief Executes one iteration of the algorithm.</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a69f0ff106139edf53b967e7c8634471b">  143</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a69f0ff106139edf53b967e7c8634471b" title="Executes one iteration of the algorithm.">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment"></span> </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment">    /// \brief Access the current variance.</span></div>
<div class="foldopen" id="foldopen00146" data-start="{" data-end="}">
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973">  146</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a>;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    }</div>
</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment"></span> </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span><span class="comment">    /// \brief Set the variance to a vector.</span></div>
<div class="foldopen" id="foldopen00151" data-start="{" data-end="}">
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a432462d9c570244fc90d6c366f495a4f">  151</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a432462d9c570244fc90d6c366f495a4f" title="Set the variance to a vector.">setVariance</a>(RealVector <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        assert(<a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>.size() == <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a>.size());</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a> = <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment"></span> </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">    /// \brief Set all variance values.</span></div>
<div class="foldopen" id="foldopen00157" data-start="{" data-end="}">
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#ae447f3ff67ff73b202425d58d9041b1e">  157</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#ae447f3ff67ff73b202425d58d9041b1e" title="Set all variance values.">setVariance</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>){</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a> = blas::repeat(<a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a9c503c7cad4ee2922f3536f1e01e7973" title="Access the current variance.">variance</a>,<a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a>.size());</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>    }</div>
</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment"></span> </div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment">    /// \brief Access the current population mean.</span></div>
<div class="foldopen" id="foldopen00162" data-start="{" data-end="}">
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#afd392abee54cf3881c031afa6b375ff9">  162</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#afd392abee54cf3881c031afa6b375ff9" title="Access the current population mean.">mean</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4220ef22c8785fec58929087aca4f340" title="The mean of the population.">m_mean</a>;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    }</div>
</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment"></span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment">    /// \brief Returns the size of the parent population.</span></div>
<div class="foldopen" id="foldopen00167" data-start="{" data-end="}">
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a6644bf8a37821ba5bf71c442b99d61ab">  167</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a6644bf8a37821ba5bf71c442b99d61ab" title="Returns the size of the parent population.">selectionSize</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#aa6bc58b439c8e2895171216060551719" title="Number of vectors chosen when updating distribution parameters.">m_selectionSize</a>;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    }</div>
</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment"></span> </div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="comment">    /// \brief Returns a mutable reference to the size of the parent population.</span></div>
<div class="foldopen" id="foldopen00172" data-start="{" data-end="}">
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#aab7855636b4af30f52c0a7157a61c420">  172</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&amp; <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#aab7855636b4af30f52c0a7157a61c420" title="Returns a mutable reference to the size of the parent population.">selectionSize</a>(){</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#aa6bc58b439c8e2895171216060551719" title="Number of vectors chosen when updating distribution parameters.">m_selectionSize</a>;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    }</div>
</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment"></span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">    /// \brief Returns a immutable reference to the size of the population.</span></div>
<div class="foldopen" id="foldopen00177" data-start="{" data-end="}">
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#aaa8990eaf10820245651f85d811f8b6b">  177</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#aaa8990eaf10820245651f85d811f8b6b" title="Returns a immutable reference to the size of the population.">populationSize</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a35d2e60b1fcd955b5e4fc630cbc1373a" title="Number of vectors sampled in a generation.">m_populationSize</a>;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    }</div>
</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="comment"></span> </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment">    /// \brief Returns a mutable reference to the size of the population.</span></div>
<div class="foldopen" id="foldopen00182" data-start="{" data-end="}">
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a554c37b09c488c12ad5df531d9e77cd2">  182</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &amp; <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a554c37b09c488c12ad5df531d9e77cd2" title="Returns a mutable reference to the size of the population.">populationSize</a>(){</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a35d2e60b1fcd955b5e4fc630cbc1373a" title="Number of vectors sampled in a generation.">m_populationSize</a>;</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    }</div>
</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment"></span> </div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">    /// \brief Set the noise type from a raw pointer.</span></div>
<div class="foldopen" id="foldopen00187" data-start="{" data-end="}">
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a3727ea4cd3507b6637c7dde5343fc6ef">  187</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#a3727ea4cd3507b6637c7dde5343fc6ef" title="Set the noise type from a raw pointer.">setNoiseType</a>( <a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html" title="Interface class for noise type.">INoiseType</a>* noiseType ) {</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#acefe67a3f3ba30e1fc0099336b536c74" title="Noise type to apply in the update of distribution parameters.">m_noise</a>.reset();</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#acefe67a3f3ba30e1fc0099336b536c74" title="Noise type to apply in the update of distribution parameters.">m_noise</a> = boost::shared_ptr&lt;INoiseType&gt; (noiseType);</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    }</div>
</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment"></span> </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">    /// \brief Get an immutable reference to Noise Type.</span></div>
<div class="foldopen" id="foldopen00193" data-start="{" data-end="}">
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#ae14731ef40261034c8d830e07ec2fbac">  193</a></span><span class="comment"></span>    <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_cross_entropy_method_1_1_i_noise_type.html" title="Interface class for noise type.">INoiseType</a> &amp;<a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#ae14731ef40261034c8d830e07ec2fbac" title="Get an immutable reference to Noise Type.">getNoiseType</a>( )<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        <span class="keywordflow">return</span> *<a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#acefe67a3f3ba30e1fc0099336b536c74" title="Noise type to apply in the update of distribution parameters.">m_noise</a>.get();</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>    }</div>
</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span> </div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span> </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#acbabcff8f468967d08c6698674ba32c1">  199</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual&lt;RealVector, double, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_cross_entropy_method.html#acbabcff8f468967d08c6698674ba32c1">IndividualType</a>;<span class="comment"></span></div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="comment">    /// \brief Updates the strategy parameters based on the supplied parent population.</span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#ace73082ea6c75482d9cb560ed983fbdd">  201</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_method.html#ace73082ea6c75482d9cb560ed983fbdd" title="Updates the strategy parameters based on the supplied parent population.">updateStrategyParameters</a>( std::vector&lt; IndividualType &gt; <span class="keyword">const</span>&amp; parents ) ;</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span> </div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#ad9ede2b41a61aed3bfb8795a4f63f005">  203</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#ad9ede2b41a61aed3bfb8795a4f63f005" title="Stores the dimensionality of the search space.">m_numberOfVariables</a>;<span class="comment">///&lt; Stores the dimensionality of the search space.</span></div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#aa6bc58b439c8e2895171216060551719">  204</a></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#aa6bc58b439c8e2895171216060551719" title="Number of vectors chosen when updating distribution parameters.">m_selectionSize</a>;<span class="comment">///&lt; Number of vectors chosen when updating distribution parameters.</span></div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a35d2e60b1fcd955b5e4fc630cbc1373a">  205</a></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a35d2e60b1fcd955b5e4fc630cbc1373a" title="Number of vectors sampled in a generation.">m_populationSize</a>;<span class="comment">///&lt; Number of vectors sampled in a generation.</span></div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span> </div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183">  207</a></span>    RealVector <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4fd98c5d17d4d963b3f478c2d3801183" title="Variance for sample parameters.">m_variance</a>;<span class="comment">///&lt; Variance for sample parameters.</span></div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span> </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>    </div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a4220ef22c8785fec58929087aca4f340">  210</a></span>    RealVector <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a4220ef22c8785fec58929087aca4f340" title="The mean of the population.">m_mean</a>;<span class="comment">///&lt; The mean of the population.</span></div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span> </div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#a17dbfb74c2056350e1bc7b643c63d6b2">  212</a></span>    <span class="keywordtype">unsigned</span> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#a17dbfb74c2056350e1bc7b643c63d6b2" title="Counter for generations.">m_counter</a>;<span class="comment">///&lt; Counter for generations.</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span> </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_method.html#acefe67a3f3ba30e1fc0099336b536c74">  214</a></span>    <a class="code hl_typedef" href="classshark_1_1_cross_entropy_method.html#a5cd00f92f182f8045837763e93fc04c6" title="Smart pointer for noise type.">StrongNoisePtr</a> <a class="code hl_variable" href="classshark_1_1_cross_entropy_method.html#acefe67a3f3ba30e1fc0099336b536c74" title="Noise type to apply in the update of distribution parameters.">m_noise</a>;<span class="comment">///&lt; Noise type to apply in the update of distribution parameters.</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>};</div>
</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>}</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span> </div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="preprocessor">#endif</span></div>
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